Rheumatoid Arthritis

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Default-person Joslyn Mangal (Author)

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WHAT IS IT?

This model demonstrates how T-regulatory (Treg) effectiveness correlates with inflammation in Rheumatoid Arthritis (RA).

LEGEND

"Y" cells = autoantibodies Grey monsters = autoreactive Th1 cells Green cells = T-regulatory cells Red squares = inflammation Yellow = the synovium

HOW IT WORKS

Rheumatoid Arthritis is an autoimmune disease. Autoimmunity occurs when the body is no longer able to differentiate between forgein invaders, such as viruses and pathogenic bacteria, from the body's own cells and tissues. This confusion causes the immune system to mistakenly attack the body's own cells and tissues. Autoantibodies and autoreactive T cells are two known contributing factors to the body attacking itself. In RA, autoantibodies and autoreactive T cells cause inflammation in the tissues that surround the joints (the synovium). The autoantibodies and autoreactive T cells can be suppressed, or deleted, by T-regulatory cells. However, in RA, it is known that the effectiveness of these T-regulatory cells can vary. Low T-regulatory effectiveness leads to a high population of autoantibodies, a high population of autoreactive T cells, and elevated inflammation - all playing a role in the progression of Rheumatoid Arthritis. Whereas, a higher T-regulatory effectiveness will enforce a lower population of autoantibodies and autoreactive T cells, in turn, showing decreased levels of inflammation.

Adjust the Treg-effectiveness slider to compare levels of inflammation, autoantibodies, and autoreactive T cells in different Treg effective enviroments.

HOW TO USE IT

SETUP: Clears the world and displays autoreactive T cells, autoantibodies, and T-regulatory cells in the presence of a synovium lining a joint within the body.

GO: Runs the simulation.

TREG-EFFECTIVENESS SLIDER: The slider displays the effectiveness of Tregs and how successful they are in deleting autoantibodies and autoreactive T cells. A high value represents an efficient Treg population and a lower value represents a less effective Treg population. The effectiveness of the Treg population has an inverse relationship with the amount of inflammation present within the synovium, as well as, the amount of autoantibodies and autoreactive T cells.

TREG-EFFECTIVENESS PLOT: Plots the number of autoantibodies and autoreactive T cells against time.

INFLAMMATION PLOT: Plots the amount of inflammation against time.

THINGS TO NOTICE

A large population of autoantibodies and autoreactive T cells, as well as, low Treg-effectiveness will display elevated levels of inflammation. The inflammation decreases as the number of autoantibodies and autoreactive T cells decreases.

THINGS TO TRY

Increasing the Treg-effectiveness slider will create a more efficient Treg population. Decreasing the Treg slider will create a dysfunctional Treg population, leading to either a slower deletion or no deletion of autoantibodies and autoreactive T cells when the Treg-effectiveness slider is at 0. This, in turn, will cause inflammation and play a role in the progression of Rheumatoid Arthritis.

EXTENDING THE MODEL

To create a more realistic Rhemuatoid Arthritis enviroment, try incorporating autoreactive effector B and T cells, as well as, adding other anti-inflammatory regulators, such as the cytokines TGFb and IL-10. Autoreactive effector B and T cells will represent proliferating autoreactive B and T cells that promote inflammation. TGFB and IL-10 will play a similar role to T-regulatory cells, where they will assist in depleting inflammation.

NETLOGO FEATURES

The "Tools-->Turtle Shapes Editor-->Import from Library" function allows you to edit the shapes of the turtles within the model

RELATED MODELS

New Villi Food Model: http://blog.modelingcommons.org/browse/onemodel/5394#modeltabsbrowseinfo

Adaptive Immunity Model: http://modelingcommons.org/browse/onemodel/5691#modeltabsbrowseinfo

CREDITS AND REFERENCES

Gift, S. and J.A. Klemens. (2017). Netlogo Adaptive Immunity Model 1.0. https://github.com/klemensj/Immune.

Wilensky, U. (2006). NetLogo Connected Chemistry 8 Gas Particle Sandbox model. http://ccl.northwestern.edu/netlogo/models/ConnectedChemistry8GasParticleSandbox. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

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Click to Run Model

breed [Synoviums Synovium]
breed [Auto-Th1s Auto-Th1]
breed [Inflammations Inflammation]
breed [Tregs Treg]
breed [Autoantibodies Autoantibody]

to setup
  clear-all
  setup-patches
  set-default-shape Inflammations "square" ; inflammation is represented by red squares
  set-default-shape Auto-Th1s "monster" ; autoreactive Th1s (Auto-Th1s) are represented by grey monsters
  set-default-shape Tregs "circle" ; Tregs are represented by green circles
  set-default-shape Autoantibodies "y" ; autoantobodies are represented by a Y
   create-Inflammations 15 ; create the Inflammation, then initialize their variables
  [
    set color white
    set size 0
    setxy random-xcor random-ycor
  ]
  create-Tregs 15 ; create the Tregs, then initialize their variables
  [
    set color green
    set size 1.2
    setxy random-xcor random-ycor
  ]
  create-Auto-Th1s 35 ; create the Auto-Th1s, then initialize their variables
  [
    set color red
    set size 1.5
    setxy random-xcor random-ycor
  ]
  create-Autoantibodies 35 ; create the Autoantibodies, then initialize their variables
  [
    set color black
    set size 2
    setxy random-xcor random-ycor
  ]
  draw-Synoviums ; create the yellow Synovium that is targeted by inflammation
  reset-ticks
end 

to setup-patches
  ask patches [ set pcolor pink + 4.5 ] ; set background to light pink
end 

to go
  ask Auto-Th1s ; allow Auto-Th1s to move around
  [ wander
bind-Auto-Th1s ; Autoreactive cells cause Synovium inflammation
  ]
  ask Tregs ; allow T-regs to move around
  [ wander
  ]
  ask Autoantibodies ; allow Autoantibodies to move around
  [ wander
  ]
  remove-Auto-Th1s ; T-regs remove Auto-Th1s
  remove-Autoantibodies ; T-regs remove Autoantibodies
    tick
end 

to remove-Auto-Th1s ; Green T-regs remove monsters and inflammation subsides
  ask turtles with [color = green]
  [
    ask turtles-here [
      if color = red  [
        if ( random 100 < Treg-effectiveness) [
          die
        ]
      ]
    ]
  ]
end 

to remove-Autoantibodies ; Green T-regs remove autoantibodies and inflammation subsides
  ask turtles with [color = green]
  [
    ask turtles-here [
      if color = black  [
        if ( random 100 < Treg-effectiveness) [
          die
        ]
      ]
    ]
  ]
end 

to bind-Auto-Th1s ; formation of inflammation
  ask Auto-Th1s with [pycor = (min-pycor) and pxcor <= max-pxcor ][
    hatch 1 [
     set breed Inflammations
     set shape "square"
     set size 1.3
     set color red
  ]]
end 

to wander ; T-regs, Auto-Th1s, and Autoantbodies wander
  rt random-float 60 - random-float 60
  fd 1
end 

to draw-Synoviums ; creation of Synovium
    ask patches with [pycor = (min-pycor) and pxcor <= max-pxcor ][
    sprout 1 [
     set breed Synoviums
     set shape "square"
     set size 10
     set color yellow + 2.5
    ]
  ]

     ask patches with [pycor = (min-pycor) and pxcor = max-pxcor ][
     sprout 1 [
      set breed Synoviums
      set shape "square"
      set size 10
     set color yellow + 2.5
    ]
  ]
end 

There is only one version of this model, created almost 5 years ago by Joslyn Mangal.

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